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GATE CS 2025 Set 2 Q34 — Linear Algebra: Matrices, Determinants, System of Linear Equations

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Consider a system of linear equations PX=QP X=Q where P∈R3×3P \in \mathbb{R}^{3 \times 3} and Q∈R3×1\mathrm{Q} \in \mathbb{R}^{3 \times 1}. Suppose PP has an LU decomposition, P=LUP=L U, where
L=[100l2110l31l321] and U=[u11u12u130u22u2300u33]. L=\left[\begin{array}{ccc} 1 & 0 & 0 \\ l_{21} & 1 & 0 \\ l_{31} & l_{32} & 1 \end{array}\right] \text{ and } U=\left[\begin{array}{ccc} u_{11} & u_{12} & u_{13} \\ 0 & u_{22} & u_{23} \\ 0 & 0 & u_{33} \end{array}\right] \text{. }

Which of the following statement(s) is/are TRUE?

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Answer: A;B;C

We analyze each statement based on the properties of the LU decomposition P=LUP = LU:

  1. Statement A is TRUE: Substituting P=LUP = LU into the system PX=QPX = Q gives L(UX)=QL(UX) = Q. By setting UX=YUX = Y, we can solve the system in two steps: first solve LY=QLY = Q for YY (via forward substitution), and then solve UX=YUX = Y for XX (via back substitution).
  2. Statement B is TRUE: Since LL is a unit lower triangular matrix, its determinant is det⁡(L)=1\det(L) = 1. Using the determinant product rule, det⁡(P)=det⁡(L)det⁡(U)=det⁡(U)\det(P) = \det(L)\det(U) = \det(U). If PP is invertible, then det⁡(P)≠0\det(P) \neq 0, which implies det⁡(U)≠0\det(U) \neq 0. Thus, both LL and UU are invertible.
  3. Statement C is TRUE: If PP is singular, then det⁡(P)=0\det(P) = 0. Since det⁡(P)=det⁡(U)\det(P) = \det(U), we have det⁡(U)=0\det(U) = 0. For an upper triangular matrix UU, the determinant is the product of its diagonal elements: det⁡(U)=u11u22u33=0\det(U) = u_{11}u_{22}u_{33} = 0. This implies at least one diagonal element of UU must be zero.
  4. Statement D is FALSE: If PP is symmetric, LL and UU are generally not symmetric. Since LL is strictly lower triangular (with 11s on the diagonal) and UU is upper triangular, they can only be symmetric if they are diagonal matrices, which is not true for general symmetric matrices. Instead, for a symmetric matrix, we have U=DLTU = DL^T where DD is a diagonal matrix.

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